Wind profile (including speed and direction) prediction at different scales (short term, mid-term and long-term) plays a crucial role for efficient operation of wind turbines and wind power prediction. This problem can be approached in two different ways: one is based on statistical signal processing techniques and both linear and nonlinear (such as artificial neural networks) models can be employed either separately or combined together for profile prediction; on the other hand, wind / atmospheric flow analysis is a classical problem in Computational Fluid Dynamics (CFD) in applied mathematics, which employs various numerical methods and algorithms, although it is an extremely time-consuming process with high computational complexity.
On the CFD side, in the simulation / prediction of the atmospheric flows on the surface, one particular difficult regime is the case with stable stratification. Stable stratification leads to internal gravity waves. The interaction between the waves and turbulence remains a challenge for the modelling of turbulent atmospheric flows. Among the various issues, an important one is how to accurately account for the incoming / outgoing waves in the boundary conditions. If not properly handled, artificial waves can be generated in the simulations, which could destabilize the simulations.
On the other hand, the signal process methods developed in Electronic and Electrical Engineering (EEE) at Sheffield are particularly suitable for capturing the wave components in a noisy signal. Therefore, the synergy between the two approaches can be particularly valuable for the simulation / prediction of wind profile / atmospheric flows.
Objectives
The aim of this project is to develop efficient and effective algorithms for wind profile prediction based on synergies between the signal processing approach and the computational fluid dynamics approach. One of the main deliverables will be a PhD thesis which contains the source code and prediction methodology details.
